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# CaseLaw dataset to assist with Law-Research - EDA
---
<dl>
<dt>Acquiring the dataset</dt>
<dd>We initially use dataset of all cases in USA to be able to train it and as a proof of concept.</dd>
<dd>The dataset is available in XML format, which we will put in mongodb or firebase format based on how unstructured ... | github_jupyter |
TSG034 - Livy logs
==================
Description
-----------
Steps
-----
### Parameters
```
import re
tail_lines = 500
pod = None # All
container = 'hadoop-livy-sparkhistory'
log_files = [ '/var/log/supervisor/log/livy*' ]
expressions_to_analyze = [
re.compile(".{17} WARN "),
re.compile(".{17} ERROR ")
... | github_jupyter |
# Training and Evaluating Machine Learning Models in cuML
This notebook explores several basic machine learning estimators in cuML, demonstrating how to train them and evaluate them with built-in metrics functions. All of the models are trained on synthetic data, generated by cuML's dataset utilities.
1. Random Fores... | github_jupyter |
# Radiative Cores & Convective Envelopes
Analysis of how magnetic fields influence the extent of radiative cores and convective envelopes in young, pre-main-sequence stars.
Begin with some preliminaries.
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
... | github_jupyter |
DIFAX Replication
=================
This example replicates the traditional DIFAX images for upper-level
observations.
By: Kevin Goebbert
Observation data comes from Iowa State Archive, accessed through the
Siphon package. Contour data comes from the GFS 0.5 degree analysis.
Classic upper-level data of Geopotential ... | github_jupyter |
# Description
This notebook contains the interpretation of a cluster (which features/latent variables in the original data are useful to distinguish traits in the cluster).
See section [LV analysis](#lv_analysis) below
# Modules loading
```
%load_ext autoreload
%autoreload 2
import pickle
import re
from pathlib imp... | github_jupyter |
# Lecture 55: Adversarial Autoencoder for Classification
## Load Packages
```
%matplotlib inline
import os
import math
import torch
import itertools
import torch.nn as nn
import torch.optim as optim
from IPython import display
import torch.nn.functional as F
import matplotlib.pyplot as plt
import torchvision.datasets... | github_jupyter |
```
from collections import defaultdict
import pyspark.sql.types as stypes
import operator
import math
d = sc.textFile("gs://lbanor/dataproc_example/data/2017-11-01").zipW
r = (sc.textFile("gs://lbanor/dataproc_example/data/2017-11-01").zipWithIndex()
.filter(lambda x: x[1] > 0)
.map(lambda x: x[0].split(',')... | github_jupyter |
```
import os
import json
import tensorflow as tf
import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import cm
from tensor2tensor import problems
from tensor2tensor import models
from tensor2tensor.bin import t2t_decoder # To register the h... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# Feature Engineering and Labeling
We'll use the price-volume data and generate features that we can feed into a model. We'll use this notebook for all the coding exercises of this lesson, so please open this notebook in a separate tab of your browser.
Please run the following code up to and including "Make Factor... | github_jupyter |
# Introduction to Linear Algebra
This is a tutorial designed to introduce you to the basics of linear algebra.
Linear algebra is a branch of mathematics dedicated to studying the properties of matrices and vectors,
which are used extensively in quantum computing to represent quantum states and operations on them.
This... | github_jupyter |
Author: Vo, Huynh Quang Nguyen
# Acknowledgments
The contents of this note are based on the lecture notes and the materials from the sources below. All rights reserved to respective owners.
1. **Deep Learning** textbook by Dr Ian Goodfellow, Prof. Yoshua Bengio, and Prof. Aaron Courville. Available at: [Deep Learnin... | github_jupyter |
```
import pandas as pd
import numpy as np
from analysis_utils import *
PAREDAO = "paredao13"
CAND1_PATH = "data/paredao13/flay.csv"
CAND2_PATH = "data/paredao13/thelma.csv"
CAND3_PATH = "data/paredao13/babu.csv"
DATE = 3
IGNORE_HASHTAGS = ["#bbb20", "#redebbb", "#bbb2020"]
candidate1_df = pd.read_csv(CAND1_PATH)
candi... | github_jupyter |
## Installation
```
!pip install -q --upgrade transformers datasets tokenizers
!pip install -q emoji pythainlp sklearn-pycrfsuite seqeval
!rm -r thai2transformers thai2transformers_parent
!git clone -b dev https://github.com/vistec-AI/thai2transformers/
!mv thai2transformers thai2transformers_parent
!mv thai2transfo... | github_jupyter |
# Pre-Processing Methods
```
%%capture
!pip3 install sparqlwrapper
# Common methods to retrieve data from Wikidata
import time
from SPARQLWrapper import SPARQLWrapper, JSON
import pandas as pd
import urllib.request as url
import json
from SPARQLWrapper import SPARQLWrapper
wiki_sparql = SPARQLWrapper("https://quer... | github_jupyter |
```
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import numexpr as ne
from scipy.ndimage import correlate1d
from dphutils import scale
import scipy.signal
from timeit import Timer
import pyfftw
# test monkey patching (it doesn't work for rfftn)
a = pyfftw.empty_aligned((512, 512), dtype='comp... | github_jupyter |
# Using matplotlib basemap to project California data
```
%matplotlib inline
import pandas as pd, numpy as np, matplotlib.pyplot as plt
from geopandas import GeoDataFrame
from mpl_toolkits.basemap import Basemap
from shapely.geometry import Point
# define basemap colors
land_color = '#F6F6F6'
water_color = '#D2F5FF'
c... | github_jupyter |
## Summarize all common compounds and their percent strong scores
```
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(patchwork))
source("viz_themes.R")
source("plotting_functions.R")
source("data_functions.R")
results_dir <- file.... | github_jupyter |
# Parameter Values
In this notebook, we explain how parameter values are set for a model. Information on how to add parameter values is provided in our [online documentation](https://pybamm.readthedocs.io/en/latest/tutorials/add-parameter-values.html)
## Setting up parameter values
```
%pip install pybamm -q # in... | github_jupyter |
# Classifying Ionosphere structure using K nearest neigbours algorithm
<hr>
### Nearest neighbors
Amongst the standard machine algorithms, Nearest neighbors is perhaps one of the most intuitive algorithms. To predict the class of a new sample, we look through the training dataset for the samples that are most similar ... | github_jupyter |
```
#load watermark
%load_ext watermark
%watermark -a 'Gopala KR' -u -d -v -p watermark,numpy,pandas,matplotlib,nltk,sklearn,tensorflow,theano,mxnet,chainer,seaborn,keras,tflearn,bokeh,gensim
from preamble import *
%matplotlib inline
```
## Algorithm Chains and Pipelines
```
from sklearn.svm import SVC
from sklearn.d... | github_jupyter |
[](https://colab.research.google.com/github/jfcrenshaw/pzflow/blob/main/examples/marginalization.ipynb)
If running in Colab, to switch to GPU, go to the menu and select Runtime -> Change runtime type -> Hardware accelerator -> GPU.
In addition,... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
import os
from PIL import Image
import PIL.ImageOps
from skimage.morphology import watershed
from skimage.feature import peak_local_max
from skimage.filters import threshold_otsu
from skimage.morphology import binary_closing
f... | github_jupyter |
# Rerank with MonoT5
```
!nvidia-smi
from pygaggle.rerank.base import Query, Text
from pygaggle.rerank.transformer import MonoT5
from trectools import TrecRun
import ir_datasets
monoT5Reranker = MonoT5()
DIR='/mnt/ceph/storage/data-in-progress/data-teaching/theses/wstud-thesis-probst/retrievalExperiments/runs-ecir22... | github_jupyter |
# GitHub : Le réseau social des développeurs grâce à Git
_Auteur_: Hugo Ducommun
_Date_: 30 Mai 2019
_GitHub_ est un plateforme de projets de jeunes développeurs motivés qui souhaient publier leur travail de manière libre (OpenSource). _GitHub_ est connu pour être pratique lorsqu'on travaille en équipe. Il permet à ... | github_jupyter |
<a href="https://colab.research.google.com/github/davemcg/scEiaD/blob/master/colab/cell_type_ML_labelling.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Auto Label Retinal Cell Types
## tldr
You can take your (retina) scRNA data and fairly qui... | github_jupyter |
## The QLBS model for a European option
Welcome to your 2nd assignment in Reinforcement Learning in Finance. In this exercise you will arrive to an option price and the hedging portfolio via standard toolkit of Dynamic Pogramming (DP).
QLBS model learns both the optimal option price and optimal hedge directly from tra... | github_jupyter |
### Installation
`devtools::install_github("zji90/SCRATdatahg19")`
`source("https://raw.githubusercontent.com/zji90/SCRATdata/master/installcode.R")`
### Import packages
```
library(devtools)
library(GenomicAlignments)
library(Rsamtools)
library(SCRATdatahg19)
library(SCRAT)
```
### Obtain Feature Matrix
```
st... | github_jupyter |
# Задание 2.1 - Нейронные сети
В этом задании вы реализуете и натренируете настоящую нейроную сеть своими руками!
В некотором смысле это будет расширением прошлого задания - нам нужно просто составить несколько линейных классификаторов вместе!
<img src="https://i.redd.it/n9fgba8b0qr01.png" alt="Stack_more_layers" wi... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import statistics
from scipy import stats
buldy_RGG_50_rep100_045 = pd.read_csv('Raw_data/Processed/proc_buldy_RGG_50_rep100_045.csv')
del buldy_RGG_50_rep100_045['Unnamed: 0']
buldy_RGG_50_rep100_045
buldy_RGG_50_rep100_067 = pd.read_csv('pro... | github_jupyter |
# Access Computation
This tutorial demonstrates how to compute access.
## Setup
```
import numpy as np
import pandas as pd
import plotly.graph_objs as go
from ostk.mathematics.objects import RealInterval
from ostk.physics.units import Length
from ostk.physics.units import Angle
from ostk.physics.time import Scale... | github_jupyter |
```
import tensorflow as tf
from tensorflow.keras import models
import numpy as np
import matplotlib.pyplot as plt
class myCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
#creating a callback function that activates if the accuracy is greater than 60%
if(logs.get('accuracy... | github_jupyter |
# TV Script Generation
In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) of scripts from 27 seasons. The Neural Network you'll build will gen... | github_jupyter |
## Importing Libraries & getting Data
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
data = pd.read_csv("dataset/winequalityN.csv")
data.head()
data.info()
data.describe()
data.columns
columns = ['type', 'fix... | github_jupyter |
```
from fastai import *
from fastai.vision import *
from fastai.callbacks import *
from fastai.utils.mem import *
from fastai.vision.gan import *
from PIL import Image
import numpy as np
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.data.... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Project: **Finding Lane Lines on the Road**
***
In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really j... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '3'
import numpy as np
import tensorflow as tf
import json
with open('dataset-bpe.json') as fopen:
data = json.load(fopen)
train_X = data['train_X']
train_Y = data['train_Y']
test_X = data['test_X']
test_Y = data['test_Y']
EOS = 2
GO = 1
vocab_size = 32000
train_Y ... | github_jupyter |
# Workshop 2: Regression and Neural Networks
https://github.com/Imperial-College-Data-Science-Society/workshops
1. Introduction to Data Science
2. **Regression and Neural Networks**
3. Classifying Character and Organ Images
4. Demystifying Causality and Causal Inference
5. A Primer to Data Engineering
6. Natural Lang... | github_jupyter |
```
%matplotlib widget
from pathlib import Path
from collections import namedtuple
import matplotlib.pyplot as plt
import numpy as np
from numpy.linalg import svd
import imageio
from scipy import ndimage
import h5py
import stempy.io as stio
import stempy.image as stim
# Set up Cori paths
ncemhub = Path('/global/cfs... | github_jupyter |
```
# input
# pmid list: ../../data/ft_info/ft_id_lst.csv
# (ft json file) ../../data/raw_data/ft/
# (ft abs file) ../../data/raw_data/abs/
# result file at ../../data/raw_data/ft/T0 (all section)
# ../../data/raw_data/ft/T1 (no abs), etc
# setp 1 download full-text
import pandas as pd
import pickle
i... | github_jupyter |
```
from __future__ import print_function
import warnings
warnings.filterwarnings(action='ignore')
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# Interpreting Neural Network Weights
Neural nets (especially deep neural nets) are some of the most powerful machine learning algorithms available. However, it can be difficult to understand (intuitively) how they work.
In the first part of this notebook, I highlight the connection between neural networks and tem... | github_jupyter |
# Qcodes example with Alazar ATS 9360
```
# import all necessary things
%matplotlib nbagg
import qcodes as qc
import qcodes.instrument.parameter as parameter
import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver
import qcodes.instrument_drivers.AlazarTech.ATS_acquisition_controllers as ats_contr
```
First... | github_jupyter |
[//]: #
<img src="idaes_icon.png" width="100">
<h1><center>Welcome to the IDAES Stakeholder Workshop</center></h1>
Welcome and thank you for taking the time to attend today's workshop. Today we will introduce you to the fundamentals of working with the IDAES process modeling toolset, and w... | github_jupyter |
# Lazy Mode and Logging
So far, we have seen Ibis in interactive mode. Interactive mode (also known as eager mode) makes Ibis return the
results of an operation immediately.
In most cases, instead of using interactive mode, it makes more sense to use the default lazy mode.
In lazy mode, Ibis won't be executing the op... | github_jupyter |
## Mutual information
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import mutual_info_classif, mutual_info_regression
from sklearn.feature_selection import SelectKBest, SelectPercentile
```
## Read Data
... | github_jupyter |
# Plotting
In this notebook, I'll develop a function to plot subjects and their labels.
```
from astropy.coordinates import SkyCoord
import astropy.io.fits
import astropy.wcs
import h5py
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
import numpy
import skimage.exposure
import sklearn.neighbors
impo... | github_jupyter |
# Single Beam
This notebook will run the ISR simulator with a set of data created from a function that makes test data. The results along with error bars are plotted below.
```
%matplotlib inline
import matplotlib.pyplot as plt
import os,inspect
from SimISR import Path
import scipy as sp
from SimISR.utilFunctions impo... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from xentropy import dihedrals
from astropy import units as au
```
# single Gaussian distro
## create artificial data
```
data= np.random.randn(100000)*30
```
## perform kde
```
dih_ent = dihedrals.dihedralEntropy(data=data,verbose=True)
dih_ent.calculate()
``... | github_jupyter |
```
"""
This notebook contains codes to run hyper-parameter tuning using a genetic algorithm.
Use another notebook if you wish to use *grid search* instead.
# Under development.
"""
import os, sys
import numpy as np
import pandas as pd
import tensorflow as tf
import sklearn
from sklearn.model_selection import train_tes... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Demo-of-RISE-for-slides-with-Jupyter-notebooks-(Python)" data-toc-modified-id="Demo-of-RISE-for-slides-with-Jupyter-notebooks-(Python)-1"><span class="toc-item-num">1 </span>Demo of RISE for slid... | github_jupyter |
```
# If you run on colab uncomment the following line
#!pip install git+https://github.com/clementchadebec/benchmark_VAE.git
import torch
import torchvision.datasets as datasets
%load_ext autoreload
%autoreload 2
mnist_trainset = datasets.MNIST(root='../../data', train=True, download=True, transform=None)
train_data... | github_jupyter |
# **PointRend - Image Segmentation as Rendering**
**Authors: Alexander Kirillov, Yuxin Wu, Kaiming H,e Ross Girshick - Facebook AI Research (FAIR)**
**Official Github**: https://github.com/facebookresearch/detectron2/tree/main/projects/PointRend
---
**Edited By Su Hyung Choi (Key Summary & Code Practice)**
If you ... | github_jupyter |
<div style='background-image: url("share/baku.jpg") ; padding: 0px ; background-size: cover ; border-radius: 15px ; height: 250px; background-position: 0% 80%'>
<div style="float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.9) ; width: 50% ; height: 150px">
<div style="positi... | github_jupyter |
# Simple Analysis with Pandas and Numpy
***ABSTRACT***
* If a donor gives aid for a project that the recipient government would have undertaken anyway, then the aid is financing some expenditure other than the intended project. The notion that aid in this sense may be "fungible," while long recognized, has recently be... | github_jupyter |
# Word vectors (FastText) for Baseline
#### Create Spacy model from word vectors
```bash
python -m spacy init-model en output/cord19_docrel/spacy/en_cord19_fasttext_300d --vectors-loc output/cord19_docrel/cord19.fasttext.w2v.txt
python -m spacy init-model en output/acl_docrel/spacy/en_acl_fasttext_300d --vectors-loc ... | github_jupyter |
```
!pip install tf-nightly-2.0-preview
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
print(tf.__version__)
def plot_series(time, series, format="-", start=0, end=None):
plt.plot(time[start:end], series[start:end], format)
plt.xlabel("Time")
plt.ylabel("Value")
plt.grid(Fals... | github_jupyter |
<table>
<tr><td align="right" style="background-color:#ffffff;">
<img src="../images/logo.jpg" width="20%" align="right">
</td></tr>
<tr><td align="right" style="color:#777777;background-color:#ffffff;font-size:12px;">
Abuzer Yakaryilmaz | April 30, 2019 (updated)
</td></tr>
<tr><td... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
%matplotlib inline
isolados = pd.read_csv('data/01 - geral_normalizada.csv')
isolados.sample(5)
df = pd.read_csv('data/02 - reacoes_normalizada.csv', names=['Ano','CCR','Composto','Resultado'], header=None, index_col=0)
df... | github_jupyter |
# GSD: Rpb1 orthologs in 1011 genomes collection
This collects Rpb1 gene and protein sequences from a collection of natural isolates of sequenced yeast genomes from [Peter et al 2017](https://www.ncbi.nlm.nih.gov/pubmed/29643504), and then estimates the count of the heptad repeats. It builds directly on the notebook [... | github_jupyter |
```
#pip install seaborn
```
# Import Libraries
```
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
```
# Read the CSV and Perform Basic Data Cleaning
```
# Raw dataset drop NA
df = pd.read_csv("../resources/train_predict.csv")
# Drop the null columns ... | github_jupyter |
## Dependencies
```
import glob
import numpy as np
import pandas as pd
from transformers import TFDistilBertModel
from tokenizers import BertWordPieceTokenizer
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Input, Dropout, GlobalAveragePooling1D, Concatenat... | github_jupyter |
## Importing necessary library
```
import snscrape.modules.twitter as sntwitter
import pandas as pd
import itertools
import plotly.graph_objects as go
from datetime import datetime
```
## Creating a data frame called "df" for storing the data to be scraped. Here, "2019 Elections" was the search keyword"
```
df = pd... | github_jupyter |
# 머신 러닝 교과서 3판
# HalvingGridSearchCV
### 경고: 이 노트북은 사이킷런 0.24 이상에서 실행할 수 있습니다.
```
# 코랩에서 실행할 경우 최신 버전의 사이킷런을 설치합니다.
!pip install --upgrade scikit-learn
import pandas as pd
df = pd.read_csv('https://archive.ics.uci.edu/ml/'
'machine-learning-databases'
'/breast-cancer-wisconsin/wdb... | github_jupyter |
# RadiusNeighborsRegressor with MinMaxScaler & Polynomial Features
**This Code template is for the regression analysis using a RadiusNeighbors Regression and the feature rescaling technique MinMaxScaler along with Polynomial Features as a feature transformation technique in a pipeline**
### Required Packages
```
imp... | github_jupyter |
# Read Washington Medicaid Fee Schedules
The Washington state Health Care Authority website for fee schedules is [here](http://www.hca.wa.gov/medicaid/rbrvs/Pages/index.aspx).
* Fee schedules come in Excel format
* Fee schedules are *usually* biannual (January and July)
* Publicly available fee schedules go back to J... | github_jupyter |
# Convolutional Neural Networks
---
In this notebook, we train a **CNN** to classify images from the CIFAR-10 database.
The images in this database are small color images that fall into one of ten classes; some example images are pictured below.
![cifar data](https://github.com/lbleal1/deep-learning-v2-pytorch/blob/m... | github_jupyter |
# **CatBoost**
### За основу взят ноутбук из вебинара "CatBoost на больших данных", канал Karpov.Courses, ведущий вебинара Александр Савченко
Репозиторий с исходником: https://github.com/AlexKbit/pyspark-catboost-example
```
%%capture
!pip install pyspark==3.0.3
from pyspark.ml import Pipeline
from pyspark.ml.featur... | github_jupyter |
---
### Universidad de Costa Rica
#### IE0405 - Modelos Probabilísticos de Señales y Sistemas
---
# `Py4` - *Librerías de manipulación de datos*
> **Pandas**, en particular, es una útil librería de manipulación de datos que ofrece estructuras de datos para el análisis de tablas numéricas y series de tiempo. Esta es u... | github_jupyter |
```
import os
import sys
import numpy as np
import cv2
from data_loader import *
from fbs_config import TrainFBSConfig, InferenceFBSConfig
from fbs_dataset import FBSDataset
from mrcnn import model as modellib
from datahandler import DataHandler
from sklearn.metrics import f1_score
from scipy.ndimage import _ni_supp... | github_jupyter |
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<div style="float:right; width:98 px; height:98px;">
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<h1>Intermediate NumPy</h1>
<h3>Unidata Python Workshop</h3... | github_jupyter |
<h1> 2c. Refactoring to add batching and feature-creation </h1>
In this notebook, we continue reading the same small dataset, but refactor our ML pipeline in two small, but significant, ways:
<ol>
<li> Refactor the input to read data in batches.
<li> Refactor the feature creation so that it is not one-to-one with inpu... | github_jupyter |
```
import torch
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
import numpy as np
import pickle
from collections import namedtuple
from tqdm import tqdm
import torch
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
import torch.nn as nn
import torch.n... | github_jupyter |
<img alt="Colaboratory logo" height="45px" src="https://colab.research.google.com/img/colab_favicon.ico" align="left" hspace="10px" vspace="0px">
<h1>Welcome to Colaboratory!</h1>
Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud.
With Colaboratory you can writ... | github_jupyter |
# PyTorch
# Intro to Neural Networks
Lets use some simple models and try to match some simple problems
```
import numpy as np
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
import matplotlib.pyplot as plt
```
### Data Loading
Before we dive deep into the nerual net, lets take a brief as... | github_jupyter |
<a href="https://colab.research.google.com/github/WuilsonEstacio/Procesamiento-de-lenguaje-natural/blob/main/codigo_para_abrir_y_contar_palabras_de_archivos.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# para leer un archivo
archivo = open('/... | github_jupyter |
```
fuelNeeded = 42/1000
tank1 = 36/1000
tank2 = 6/1000
tank1 + tank2 >= fuelNeeded
from decimal import Decimal
fN = Decimal(fuelNeeded)
t1 = Decimal(tank1)
t2 = Decimal(tank2)
t1 + t2 >= fN
class Rational(object):
def __init__ (self, num, denom):
self.numerator = num
self.denominator = denom
... | github_jupyter |
# Week 7 worksheet: Spherically symmetric parabolic PDEs
This worksheet contains a number of exercises covering only the numerical aspects of the course. Some parts, however, still require you to solve the problem by hand, i.e. with pen and paper. The rest needs you to write pythob code. It should usually be obvious w... | github_jupyter |
```
from pymongo import MongoClient
import pandas as pd
import datetime
# Open Database and find history data collection
client = MongoClient()
db = client.test_database
shdaily = db.indexdata
# KDJ calculation formula
def KDJCalculation(K1, D1, high, low, close):
# input last K1, D1, max value, min value and cur... | github_jupyter |
<a href="https://colab.research.google.com/github/julianox5/Desafios-Resolvidos-do-curso-machine-learning-crash-course-google/blob/master/numpy_para_machine_learning.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Importando o numpy
```
import nump... | github_jupyter |
# Doom Deadly Corridor with Dqn
The purpose of this scenario is to teach the agent to navigate towards his fundamental goal (the vest) and make sure he survives at the same time.
### Enviroment
Map is a corridor with shooting monsters on both sides (6 monsters in total). A green vest is placed at the oposite end of t... | github_jupyter |
# TRTR Dataset D
```
#import libraries
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
import os
print('Libraries imported!!')
#define directory of functions and actual directory
HOME_PATH = '' #home path of the project
FUNCTIONS_DIR = 'EVALUATION FUNCTIONS/UTILITY'
ACTUAL_DIR ... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Join
Copyright (c) Microsoft Corporation. All rights reserved.<br>
Licensed under the MIT License.<br>
In Data Prep you can easily join two D... | github_jupyter |
# Credit Risk Resampling Techniques
```
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
from pathlib import Path
from collections import Counter
```
# Read the CSV and Perform Basic Data Cleaning
```
columns = [
"loan_amnt", "int_rate", "installment", "home_ownership",
... | github_jupyter |
# <font color='Purple'>Gravitational Wave Generation Array</font>
A Phase Array of dumbells can make a detectable signal...
#### To do:
1. Calculate the dumbell parameters for given mass and frequency
1. How many dumbells?
1. Far-field radiation pattern from many radiators.
1. Beamed GW won't be a plane wave. So what... | github_jupyter |
# Introduction to Linear Regression
*Adapted from Chapter 3 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)*
||continuous|categorical|
|---|---|---|
|**supervised**|**regression**|classification|
|**unsupervised**|dimension reduction|clustering|
## Motivation
Why are we learning li... | github_jupyter |
# Testing Configurations
The behavior of a program is not only governed by its data. The _configuration_ of a program – that is, the settings that govern the execution of a program on its (regular) input data, as set by options or configuration files – just as well influences behavior, and thus can and should be test... | github_jupyter |
# The Discrete Fourier Transform
*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Comunications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## ... | github_jupyter |
# Concise Implementation of Linear Regression
:label:`sec_linear_concise`
Broad and intense interest in deep learning for the past several years
has inspired companies, academics, and hobbyists
to develop a variety of mature open source frameworks
for automating the repetitive work of implementing
gradient-based learn... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.float_format', lambda x: '%.4f' % x)
import seaborn as sns
sns.set_context("paper", font_scale=1.3)
sns.set_style('white')
import warnings
warnings.filterwarnings('ignore')
from time import time
import matplotlib.ticker as... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/husein/t5/prepare/mesolitica-tpu.json'
import malaya_speech.train.model.conformer as conformer
import malaya_speech.train.model.transducer as transducer
import malaya_speech
import tensorflow as tf
import numpy a... | github_jupyter |
```
import datafaucet as dfc
# start the engine
project = dfc.project.load()
spark = dfc.context()
df = spark.range(100)
df.data.grid()
(df
.cols.get('name').obscure(alias='enc')
.cols.get('enc').unravel(alias='dec')
).data.grid()
df.data.grid().groupby(['id', 'name'])\
.agg({'fight':[max, 'min'], 'trade': ... | github_jupyter |
# Sonar - Decentralized Model Training Simulation (local)
DISCLAIMER: This is a proof-of-concept implementation. It does not represent a remotely product ready implementation or follow proper conventions for security, convenience, or scalability. It is part of a broader proof-of-concept demonstrating the vision of the... | github_jupyter |
# NSCI 801 - Quantitative Neuroscience
## Reproducibility, reliability, validity
Gunnar Blohm
### Outline
* statistical considerations
* multiple comparisons
* exploratory analyses vs hypothesis testing
* Open Science
* general steps toward transparency
* pre-registration / registered report
* Open sci... | github_jupyter |
<a href="https://colab.research.google.com/github/krmiddlebrook/intro_to_deep_learning/blob/master/machine_learning/mini_lessons/image_data.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Processing Image Data
Computer vision is a field of machine... | github_jupyter |
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